
We show that open-vocabulary neural machine translation is possible by encoding (rare) words via subword units.
We find our architecture simpler and more effective than using large vocabularies and back-off dictionaries [1]}, [2]}.

We adapt byte pair encoding (BPE) [3]}, a compression algorithm, to the task of word segmentation.
BPE allows for the representation of an open vocabulary through a fixed-size vocabulary of variable-length character sequences,
making it a very suitable word segmentation strategy for neural network models.

